{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "873bfffa-7848-4bd2-ac31-c50138107831",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas  as  pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0951c54b-c3e0-40e4-b229-49362730000f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>POS</th>\n",
       "      <th>ID</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>INFO</th>\n",
       "      <th>FORMAT</th>\n",
       "      <th>X</th>\n",
       "      <th>CYP21A1P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037593</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,1...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32037593_T_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037598</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,1...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32037598_T_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037604</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32037604_A_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037977</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32037977_C_CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038115</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038115_G_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045913</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32045913_T_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046056</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32046056_G_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046067</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32046067_G_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046069</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32046069_G_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046114</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32046114_C_A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM       POS ID REF ALT  QUAL FILTER  \\\n",
       "0   chr6  32037593  .   T   C     0      .   \n",
       "1   chr6  32037598  .   T   C     0      .   \n",
       "2   chr6  32037604  .   A   G     0      .   \n",
       "3   chr6  32037977  .   C  CA     0      .   \n",
       "4   chr6  32038115  .   G   C     0      .   \n",
       "..   ...       ... ..  ..  ..   ...    ...   \n",
       "94  chr6  32045913  .   T   C     0      .   \n",
       "95  chr6  32046056  .   G   C     0      .   \n",
       "96  chr6  32046067  .   G   T     0      .   \n",
       "97  chr6  32046069  .   G   A     0      .   \n",
       "98  chr6  32046114  .   C   A     0      .   \n",
       "\n",
       "                                                 INFO FORMAT               X  \\\n",
       "0   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,1...     PL  60,3,0,60,3,60   \n",
       "1   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,1...     PL  60,3,0,60,3,60   \n",
       "2   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "3   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "4   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "..                                                ...    ...             ...   \n",
       "94  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "95  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "96  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "97  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "98  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "\n",
       "              CYP21A1P  \n",
       "0    chr6_32037593_T_C  \n",
       "1    chr6_32037598_T_C  \n",
       "2    chr6_32037604_A_G  \n",
       "3   chr6_32037977_C_CA  \n",
       "4    chr6_32038115_G_C  \n",
       "..                 ...  \n",
       "94   chr6_32045913_T_C  \n",
       "95   chr6_32046056_G_C  \n",
       "96   chr6_32046067_G_T  \n",
       "97   chr6_32046069_G_A  \n",
       "98   chr6_32046114_C_A  \n",
       "\n",
       "[99 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcf_file_path = \"/lustre/home/acct-medfzx/medfzx-lkw/project/CAH/CYP21A1P.check.csv\"\n",
    "\n",
    "# 找到 header 行\n",
    "with open(vcf_file_path) as f:\n",
    "    for line in f:\n",
    "        if line.startswith(\"#CHROM\"):\n",
    "            header = line.strip().lstrip(\"#\").split(\"\\t\")\n",
    "            break\n",
    "\n",
    "# 读取 VCF 数据（跳过 meta 信息行）\n",
    "df_vcf_CYP21A1P = pd.read_csv(vcf_file_path, comment=\"#\", sep=\"\\t\", names=header)\n",
    "\n",
    "df_vcf_CYP21A1P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29028499-28e4-42e0-96e1-09d0e0987fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_in_A'] = df['B'].isin(list_A)\n",
    "matched_data = df[df['is_in_A']][['C']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd0e2f3-5b19-40cd-ab66-cfe23d2d9264",
   "metadata": {},
   "source": [
    "# real vcf 3rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ad42b6f-574a-472b-80f3-f64aad854a97",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HG001\n",
      "['chr6_32038844_A_C', 'chr6_32038878_C_T', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041679_A_G', 'chr6_32045913_T_C']\n",
      "HG002\n",
      "['chr6_32038844_A_C', 'chr6_32039847_A_G', 'chr6_32039848_C_T', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041679_A_G', 'chr6_32043867_A_G', 'chr6_32043937_A_G', 'chr6_32044177_T_C', 'chr6_32044184_A_G', 'chr6_32045913_T_C']\n",
      "HG003\n",
      "['chr6_32038127_T_C', 'chr6_32038128_A_C', 'chr6_32038139_A_G', 'chr6_32038141_T_G', 'chr6_32038844_A_C', 'chr6_32039027_C_A', 'chr6_32039046_A_G', 'chr6_32039050_G_T', 'chr6_32039055_C_G', 'chr6_32039056_A_G', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041502_T_C', 'chr6_32041524_A_G', 'chr6_32041574_C_T', 'chr6_32041577_T_C', 'chr6_32041598_T_C', 'chr6_32041608_C_T', 'chr6_32041679_A_G', 'chr6_32042412_A_C', 'chr6_32042415_C_CT', 'chr6_32043937_A_G', 'chr6_32044177_T_C', 'chr6_32044184_A_G', 'chr6_32045913_T_C']\n",
      "HG004\n",
      "['chr6_32039847_A_G', 'chr6_32039848_C_T', 'chr6_32043867_A_G', 'chr6_32043937_A_G', 'chr6_32044177_T_C', 'chr6_32044184_A_G']\n",
      "HG005\n",
      "['chr6_32038844_A_C', 'chr6_32038878_C_T', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041679_A_G', 'chr6_32044177_T_C', 'chr6_32044184_A_G', 'chr6_32045913_T_C']\n",
      "HG006\n",
      "['chr6_32044177_T_C', 'chr6_32044184_A_G']\n",
      "HG007\n",
      "['chr6_32038844_A_C', 'chr6_32038878_C_T', 'chr6_32038983_TG_T', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041679_A_G', 'chr6_32043937_A_G', 'chr6_32045913_T_C']\n"
     ]
    }
   ],
   "source": [
    "fileList = os.listdir(\"/lustre/home/acct-medfzx/medfzx-lkw/project/2rd_NGS_annovar_LD/data/HG00X/HG00X_vcf\")\n",
    "fileList.sort()\n",
    "vcfFileList = [x for x in fileList if x.lower().endswith('.vcf')]\n",
    "\n",
    "for vcfFile in vcfFileList[:]:\n",
    "    print(vcfFile.split('_')[0])\n",
    "    vcfFilePath = os.path.join(\"/lustre/home/acct-medfzx/medfzx-lkw/project/2rd_NGS_annovar_LD/data/HG00X/HG00X_vcf\", vcfFile)\n",
    "    with open(vcfFilePath) as f:\n",
    "        for line in f:\n",
    "            if line.startswith(\"#CHROM\"):\n",
    "                header = line.strip().lstrip(\"#\").split(\"\\t\")\n",
    "                break\n",
    "    # 读取 VCF 数据（跳过 meta 信息行）\n",
    "    df = pd.read_csv(vcfFilePath, comment=\"#\", sep=\"\\t\", names=header)\n",
    "    df[\"variant_id\"] = df[\"CHROM\"].astype(str) + \"_\" + df[\"POS\"].astype(str) + \"_\" + df[\"REF\"] + \"_\" + df[\"ALT\"]\n",
    "    transPos = [x for x in df[\"variant_id\"] if x in df_vcf_CYP21A1P[\"CYP21A1P\"].tolist()]\n",
    "    \n",
    "    print(transPos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "42c38238-87ed-43a2-89ce-1f7b86673302",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>POS</th>\n",
       "      <th>ID</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>INFO</th>\n",
       "      <th>FORMAT</th>\n",
       "      <th>X</th>\n",
       "      <th>CYP21A1P</th>\n",
       "      <th>is_in_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038127</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038127_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038128</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038128_A_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038139</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038139_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038141</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038141_T_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038844</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038844_A_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039027</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32039027_C_A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039046</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32039046_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039050</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32039050_G_T</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039055</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32039055_C_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039056</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32039056_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041119</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041119_G_A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041127</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041127_G_A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041502</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041502_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041524</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041524_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041574</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041574_C_T</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041577</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041577_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041598</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041598_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041608</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041608_C_T</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041679</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041679_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042412</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32042412_A_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042415</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32042415_C_CT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043937</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32043937_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32044177</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32044177_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32044184</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32044184_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045913</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32045913_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM       POS ID REF ALT  QUAL FILTER  \\\n",
       "5   chr6  32038127  .   T   C     0      .   \n",
       "6   chr6  32038128  .   A   C     0      .   \n",
       "7   chr6  32038139  .   A   G     0      .   \n",
       "8   chr6  32038141  .   T   G     0      .   \n",
       "18  chr6  32038844  .   A   C     0      .   \n",
       "42  chr6  32039027  .   C   A     0      .   \n",
       "43  chr6  32039046  .   A   G     0      .   \n",
       "44  chr6  32039050  .   G   T     0      .   \n",
       "45  chr6  32039055  .   C   G     0      .   \n",
       "46  chr6  32039056  .   A   G     0      .   \n",
       "70  chr6  32041119  .   G   A     0      .   \n",
       "71  chr6  32041127  .   G   A     0      .   \n",
       "72  chr6  32041502  .   T   C     0      .   \n",
       "73  chr6  32041524  .   A   G     0      .   \n",
       "74  chr6  32041574  .   C   T     0      .   \n",
       "75  chr6  32041577  .   T   C     0      .   \n",
       "76  chr6  32041598  .   T   C     0      .   \n",
       "77  chr6  32041608  .   C   T     0      .   \n",
       "78  chr6  32041679  .   A   G     0      .   \n",
       "79  chr6  32042412  .   A   C     0      .   \n",
       "80  chr6  32042415  .   C  CT     0      .   \n",
       "88  chr6  32043937  .   A   G     0      .   \n",
       "89  chr6  32044177  .   T   C     0      .   \n",
       "90  chr6  32044184  .   A   G     0      .   \n",
       "94  chr6  32045913  .   T   C     0      .   \n",
       "\n",
       "                                                 INFO FORMAT               X  \\\n",
       "5   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "6   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "7   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "8   DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "18  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "42  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "43  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "44  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "45  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "46  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "70  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "71  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "72  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "73  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "74  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "75  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "76  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "77  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "78  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "79  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "80  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "88  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "89  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "90  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "94  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "\n",
       "              CYP21A1P  is_in_A  \n",
       "5    chr6_32038127_T_C     True  \n",
       "6    chr6_32038128_A_C     True  \n",
       "7    chr6_32038139_A_G     True  \n",
       "8    chr6_32038141_T_G     True  \n",
       "18   chr6_32038844_A_C     True  \n",
       "42   chr6_32039027_C_A     True  \n",
       "43   chr6_32039046_A_G     True  \n",
       "44   chr6_32039050_G_T     True  \n",
       "45   chr6_32039055_C_G     True  \n",
       "46   chr6_32039056_A_G     True  \n",
       "70   chr6_32041119_G_A     True  \n",
       "71   chr6_32041127_G_A     True  \n",
       "72   chr6_32041502_T_C     True  \n",
       "73   chr6_32041524_A_G     True  \n",
       "74   chr6_32041574_C_T     True  \n",
       "75   chr6_32041577_T_C     True  \n",
       "76   chr6_32041598_T_C     True  \n",
       "77   chr6_32041608_C_T     True  \n",
       "78   chr6_32041679_A_G     True  \n",
       "79   chr6_32042412_A_C     True  \n",
       "80  chr6_32042415_C_CT     True  \n",
       "88   chr6_32043937_A_G     True  \n",
       "89   chr6_32044177_T_C     True  \n",
       "90   chr6_32044184_A_G     True  \n",
       "94   chr6_32045913_T_C     True  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HG0003 = ['chr6_32038127_T_C', 'chr6_32038128_A_C', 'chr6_32038139_A_G', 'chr6_32038141_T_G', 'chr6_32038844_A_C', 'chr6_32039027_C_A', 'chr6_32039046_A_G', 'chr6_32039050_G_T', 'chr6_32039055_C_G', 'chr6_32039056_A_G', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041502_T_C', 'chr6_32041524_A_G', 'chr6_32041574_C_T', 'chr6_32041577_T_C', 'chr6_32041598_T_C', 'chr6_32041608_C_T', 'chr6_32041679_A_G', 'chr6_32042412_A_C', 'chr6_32042415_C_CT', 'chr6_32043937_A_G', 'chr6_32044177_T_C', 'chr6_32044184_A_G', 'chr6_32045913_T_C']\n",
    "df_vcf_CYP21A1P['is_in_A'] = df_vcf_CYP21A1P['CYP21A1P'].isin(HG0003)\n",
    "df_vcf_CYP21A1P[df_vcf_CYP21A1P['is_in_A'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2dde32ce-879f-4f07-9fee-5d37f3f7d107",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>POS</th>\n",
       "      <th>ID</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>INFO</th>\n",
       "      <th>FORMAT</th>\n",
       "      <th>X</th>\n",
       "      <th>CYP21A1P</th>\n",
       "      <th>is_in_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038844</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038844_A_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038878</td>\n",
       "      <td>.</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32038878_C_T</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038983</td>\n",
       "      <td>.</td>\n",
       "      <td>TG</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=0;I16=0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0;QS=0,...</td>\n",
       "      <td>PL</td>\n",
       "      <td>0,0,0</td>\n",
       "      <td>chr6_32038983_TG_T</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041119</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041119_G_A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041127</td>\n",
       "      <td>.</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041127_G_A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041679</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32041679_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043937</td>\n",
       "      <td>.</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32043937_A_G</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045913</td>\n",
       "      <td>.</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>.</td>\n",
       "      <td>DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...</td>\n",
       "      <td>PL</td>\n",
       "      <td>60,3,0,60,3,60</td>\n",
       "      <td>chr6_32045913_T_C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM       POS ID REF ALT  QUAL FILTER  \\\n",
       "18  chr6  32038844  .   A   C     0      .   \n",
       "22  chr6  32038878  .   C   T     0      .   \n",
       "33  chr6  32038983  .  TG   T     0      .   \n",
       "70  chr6  32041119  .   G   A     0      .   \n",
       "71  chr6  32041127  .   G   A     0      .   \n",
       "78  chr6  32041679  .   A   G     0      .   \n",
       "88  chr6  32043937  .   A   G     0      .   \n",
       "94  chr6  32045913  .   T   C     0      .   \n",
       "\n",
       "                                                 INFO FORMAT               X  \\\n",
       "18  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "22  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "33  DP=0;I16=0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0;QS=0,...     PL           0,0,0   \n",
       "70  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "71  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "78  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "88  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "94  DP=1;I16=0,0,1,0,0,0,60,3600,0,0,60,3600,0,0,2...     PL  60,3,0,60,3,60   \n",
       "\n",
       "              CYP21A1P  is_in_A  \n",
       "18   chr6_32038844_A_C     True  \n",
       "22   chr6_32038878_C_T     True  \n",
       "33  chr6_32038983_TG_T     True  \n",
       "70   chr6_32041119_G_A     True  \n",
       "71   chr6_32041127_G_A     True  \n",
       "78   chr6_32041679_A_G     True  \n",
       "88   chr6_32043937_A_G     True  \n",
       "94   chr6_32045913_T_C     True  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HG0007=['chr6_32038844_A_C', 'chr6_32038878_C_T', 'chr6_32038983_TG_T', 'chr6_32041119_G_A', 'chr6_32041127_G_A', 'chr6_32041679_A_G', 'chr6_32043937_A_G', 'chr6_32045913_T_C']\n",
    "df_vcf_CYP21A1P['is_in_A'] = df_vcf_CYP21A1P['CYP21A1P'].isin(HG0007)\n",
    "df_vcf_CYP21A1P[df_vcf_CYP21A1P['is_in_A'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc0032d-ac30-460c-a0eb-8749c753a218",
   "metadata": {},
   "source": [
    "# CHD depth average stand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "63b8c173-c56c-4747-b0fa-e2dfe04bfbbc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>c1941</th>\n",
       "      <th>c1947</th>\n",
       "      <th>c1953</th>\n",
       "      <th>c1957</th>\n",
       "      <th>c1959</th>\n",
       "      <th>c1965</th>\n",
       "      <th>c1978</th>\n",
       "      <th>c1989</th>\n",
       "      <th>...</th>\n",
       "      <th>c2275</th>\n",
       "      <th>c2290</th>\n",
       "      <th>c2295</th>\n",
       "      <th>c2298</th>\n",
       "      <th>c2303</th>\n",
       "      <th>c2321</th>\n",
       "      <th>c2324</th>\n",
       "      <th>c2341</th>\n",
       "      <th>c2356</th>\n",
       "      <th>c2359</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037749</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037751</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8584</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8585</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8586</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8587</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8588</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8589 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  c1941  c1947  c1953  c1957  c1959  c1965  c1978  c1989  \\\n",
       "0     chr6  32037748    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "1     chr6  32037749    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "2     chr6  32037750    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "3     chr6  32037751    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "4     chr6  32037752    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "...    ...       ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "8584  chr6  32037690    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8585  chr6  32037691    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8586  chr6  32037692    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8587  chr6  32037693    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8588  chr6  32037694    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "\n",
       "      ...  c2275  c2290  c2295  c2298  c2303  c2321  c2324  c2341  c2356  \\\n",
       "0     ...    0.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1     ...    0.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2     ...    0.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3     ...    0.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4     ...    0.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "8584  ...    0.0    0.0    0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "8585  ...    0.0    0.0    0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "8586  ...    0.0    0.0    0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "8587  ...    0.0    0.0    0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "8588  ...    0.0    0.0    0.0    0.0    0.0    2.0    0.0    0.0    0.0   \n",
       "\n",
       "      c2359  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "...     ...  \n",
       "8584    0.0  \n",
       "8585    0.0  \n",
       "8586    0.0  \n",
       "8587    0.0  \n",
       "8588    0.0  \n",
       "\n",
       "[8589 rows x 101 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHD_path = \"/lustre/home/acct-medfzx/medfzx-lkw/project/CHD/result/bam\"\n",
    "fileList = os.listdir(CHD_path)\n",
    "fileList = [x for x in fileList if x.lower().startswith('c')]\n",
    "fileList.sort()\n",
    "dfs = []\n",
    "for fileDir in fileList[:]:\n",
    "    fullDepthPath = os.path.join(CHD_path, fileDir, fileDir + \"_q20_CYP21A2.depth.tsv\") \n",
    "    df = pd.read_csv(fullDepthPath, header=None, names=[\"chr\", 'position', fileDir], sep='\\t')\n",
    "    df.set_index(['chr', 'position'], inplace=True)\n",
    "    dfs.append(df)\n",
    "\n",
    "merged_df = pd.concat(dfs, axis=1).reset_index()\n",
    "merged_df.fillna(0, inplace=True)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "6ced474c-aba4-4496-b1c0-c73334d4e2e3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>c1941</th>\n",
       "      <th>c1947</th>\n",
       "      <th>c1953</th>\n",
       "      <th>c1957</th>\n",
       "      <th>c1959</th>\n",
       "      <th>c1965</th>\n",
       "      <th>c1978</th>\n",
       "      <th>c1989</th>\n",
       "      <th>...</th>\n",
       "      <th>c2295</th>\n",
       "      <th>c2298</th>\n",
       "      <th>c2303</th>\n",
       "      <th>c2321</th>\n",
       "      <th>c2324</th>\n",
       "      <th>c2341</th>\n",
       "      <th>c2356</th>\n",
       "      <th>c2359</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.728254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037749</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.731051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.731051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037751</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.731051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.731051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8584</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.911109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8585</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434343</td>\n",
       "      <td>0.955173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8586</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.933018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8587</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434343</td>\n",
       "      <td>0.933784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8588</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32037694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.414141</td>\n",
       "      <td>0.876288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8589 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  c1941  c1947  c1953  c1957  c1959  c1965  c1978  c1989  \\\n",
       "0     chr6  32037748    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "1     chr6  32037749    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "2     chr6  32037750    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "3     chr6  32037751    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "4     chr6  32037752    1.0    0.0    0.0    0.0    0.0    2.0    0.0    2.0   \n",
       "...    ...       ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "8584  chr6  32037690    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8585  chr6  32037691    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8586  chr6  32037692    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8587  chr6  32037693    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "8588  chr6  32037694    0.0    0.0    0.0    0.0    0.0    0.0    0.0    2.0   \n",
       "\n",
       "      ...  c2295  c2298  c2303  c2321  c2324  c2341  c2356  c2359       avg  \\\n",
       "0     ...    2.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.292929   \n",
       "1     ...    2.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.303030   \n",
       "2     ...    2.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.303030   \n",
       "3     ...    2.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.303030   \n",
       "4     ...    2.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  0.303030   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...       ...   \n",
       "8584  ...    0.0    0.0    0.0    2.0    0.0    0.0    0.0    0.0  0.424242   \n",
       "8585  ...    0.0    0.0    0.0    2.0    0.0    0.0    0.0    0.0  0.434343   \n",
       "8586  ...    0.0    0.0    0.0    2.0    0.0    0.0    0.0    0.0  0.424242   \n",
       "8587  ...    0.0    0.0    0.0    2.0    0.0    0.0    0.0    0.0  0.434343   \n",
       "8588  ...    0.0    0.0    0.0    2.0    0.0    0.0    0.0    0.0  0.414141   \n",
       "\n",
       "           std  \n",
       "0     0.728254  \n",
       "1     0.731051  \n",
       "2     0.731051  \n",
       "3     0.731051  \n",
       "4     0.731051  \n",
       "...        ...  \n",
       "8584  0.911109  \n",
       "8585  0.955173  \n",
       "8586  0.933018  \n",
       "8587  0.933784  \n",
       "8588  0.876288  \n",
       "\n",
       "[8589 rows x 103 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['avg'] = merged_df.iloc[:, 2:].mean(axis=1)\n",
    "merged_df['std'] = merged_df.iloc[:, 2:].std(axis=1)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0cbd062a-3aad-465b-baf4-68188d33791c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>c2290</th>\n",
       "      <th>c1953</th>\n",
       "      <th>c2227</th>\n",
       "      <th>c1989</th>\n",
       "      <th>c2142</th>\n",
       "      <th>c2022</th>\n",
       "      <th>c2298</th>\n",
       "      <th>c2239</th>\n",
       "      <th>...</th>\n",
       "      <th>c2132</th>\n",
       "      <th>c2165</th>\n",
       "      <th>c2004</th>\n",
       "      <th>c1959</th>\n",
       "      <th>c2045</th>\n",
       "      <th>c2064</th>\n",
       "      <th>c2027</th>\n",
       "      <th>c2008</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039052</td>\n",
       "      <td>37.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.030303</td>\n",
       "      <td>17.200807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  c2290  c1953  c2227  c1989  c2142  c2022  c2298  c2239  \\\n",
       "1009  chr6  32039052   37.0   61.0   32.0   32.0   35.0   32.0   23.0   50.0   \n",
       "\n",
       "      ...  c2132  c2165  c2004  c1959  c2045  c2064  c2027  c2008        avg  \\\n",
       "1009  ...   34.0   22.0   31.0   76.0   21.0   20.0    5.0   17.0  31.030303   \n",
       "\n",
       "            std  \n",
       "1009  17.200807  \n",
       "\n",
       "[1 rows x 103 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['position'] == 32039052]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "917d923d-9318-41b1-bf64-28712a0675d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del merged_df['count_less'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "6cb61e90-4068-49c0-9f8c-5397df2d2259",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2147292/3308168220.py:6: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  merged_df[['1x_std_range', '2x_std_range', '3x_std_range']] = merged_df[['1x_std_range', '2x_std_range', '3x_std_range']].applymap(ast.literal_eval)\n"
     ]
    }
   ],
   "source": [
    "merged_df['1x_std_range'] = '(' + (merged_df['avg'] - merged_df['std']).round(2).astype(str) + ', ' + (merged_df['avg'] + merged_df['std']).round(2).astype(str) + ')'\n",
    "merged_df['2x_std_range'] = '(' + (merged_df['avg'] - 2 * merged_df['std']).round(2).astype(str) + ', ' + (merged_df['avg'] + 2 * merged_df['std']).round(2).astype(str) + ')'\n",
    "merged_df['3x_std_range'] = '(' + (merged_df['avg'] - 3 * merged_df['std']).round(2).astype(str) + ', ' + (merged_df['avg'] + 3 * merged_df['std']).round(2).astype(str) + ')'\n",
    "\n",
    "import ast\n",
    "merged_df[['1x_std_range', '2x_std_range', '3x_std_range']] = merged_df[['1x_std_range', '2x_std_range', '3x_std_range']].applymap(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "b53a0837-955e-4b4d-9580-b6641bef6775",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 示例数据\n",
    "\n",
    "# 选择列范围（从第3列到倒数第3列）\n",
    "target_columns = merged_df.columns[2:-5]\n",
    "\n",
    "# 计算每行阈值\n",
    "std1 = merged_df['avg'] - merged_df['std']\n",
    "std2 = merged_df['avg'] - 2*merged_df['std']\n",
    "std3 = merged_df['avg'] - 3*merged_df['std']\n",
    "# 统计满足条件的列数\n",
    "merged_df['LT_1std'] = merged_df[target_columns].lt(std1, axis=0).sum(axis=1)\n",
    "merged_df['LT_2std'] = merged_df[target_columns].lt(std2, axis=0).sum(axis=1)\n",
    "merged_df['LT_3std'] = merged_df[target_columns].lt(std3, axis=0).sum(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c3f4c5a4-a4bc-4395-8d91-42953d6b4855",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设 df 已经定义好\n",
    "row_index = 0  # 你想画的行\n",
    "row_data = merged_df.iloc[row_index, 2:-8]  # 第3列到倒数第3列\n",
    "\n",
    "# 转换成长格式\n",
    "row_df = row_data.reset_index()\n",
    "row_df.columns = ['Variable', 'Value']\n",
    "\n",
    "# 绘制\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.violinplot(x='Variable', y='Value', data=row_df)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0a52fadb-70a9-45af-8a9e-96f9c0ef8e53",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c2290    2.0\n",
       "c1953    0.0\n",
       "c2227    1.0\n",
       "c1989    0.0\n",
       "c2142    5.0\n",
       "        ... \n",
       "c1959    1.0\n",
       "c2045    0.0\n",
       "c2064    0.0\n",
       "c2027    0.0\n",
       "c2008    0.0\n",
       "Name: 0, Length: 99, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.iloc[row_index, 2:-8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c31c59-e687-4c5e-8287-17a16183d6f3",
   "metadata": {},
   "source": [
    "## plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f71052eb-c8eb-46ca-94e9-5cea87638b0d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f97458ec-1952-4ffd-a280-b3e85f1a887e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df['std'].plot(kind='hist', bins=30, edgecolor='black')  # 直方图\n",
    "plt.title('std')\n",
    "plt.xlabel('value')\n",
    "plt.ylabel('time2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "87c9c273-34f2-4c78-9a35-640bc4bad6e6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df['avg'].plot(kind='hist', bins=30, edgecolor='black')  # 直方图\n",
    "plt.title('avg')\n",
    "plt.xlabel('value')\n",
    "plt.ylabel('time2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372894ed-e230-43e8-aeb7-368395637b87",
   "metadata": {},
   "source": [
    "#  小提琴图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6f238c85-071d-46aa-8fd4-01cd3788f3fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 生成模拟数据\n",
    "np.random.seed(42)\n",
    "data = pd.DataFrame({\n",
    "    'Category': np.repeat(['A', 'B', 'C'], 100),  # 3个类别，每类100样本\n",
    "    'Value': np.concatenate([\n",
    "        np.random.normal(50, 10, 100),  # 类别A：均值50，标准差10\n",
    "        np.random.normal(80, 5, 100),   # 类别B：均值80，标准差5\n",
    "        np.random.gamma(2, 15, 100)     # 类别C：伽马分布\n",
    "    ])\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "980486ab-5ca0-4c47-b4d9-362961f9b952",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2147292/1719612050.py:5: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.violinplot(\n",
    "    x='Category',      # 分类变量（x轴分组）\n",
    "    y='Value',        # 数值变量（y轴分布）\n",
    "    data=data,        # 数据源\n",
    "    inner='quartile', # 内部显示四分位数线\n",
    "    palette='Set2'    # 配色方案\n",
    ")\n",
    "sns.stripplot(       # 叠加散点\n",
    "    x='Category',\n",
    "    y='Value',\n",
    "    data=data,\n",
    "    color='black',\n",
    "    size=4,\n",
    "    alpha=0.3,\n",
    "    jitter=True      # 避免点重叠\n",
    ")\n",
    "\n",
    "plt.title('vinl', fontsize=14)\n",
    "plt.xlabel('type', fontsize=12)\n",
    "plt.ylabel('data', fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c1aa41a2-8a26-4906-ae5e-03f50eefe3d0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f16e474ca70>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 新增分组变量\n",
    "data['Group'] = np.random.choice(['X', 'Y'], size=300)\n",
    "\n",
    "sns.violinplot(\n",
    "    x='Category',\n",
    "    y='Value',\n",
    "    hue='Group',      # 按Group分色\n",
    "    data=data,\n",
    "    split=True,       # 拆分小提琴（仅适用于二分类hue）\n",
    "    palette='muted'\n",
    ")\n",
    "\n",
    "sns.stripplot(       # 叠加散点\n",
    "    x='Category',\n",
    "    y='Value',\n",
    "    data=data,\n",
    "    color='black',\n",
    "    size=4,\n",
    "    alpha=0.3,\n",
    "    jitter=True      # 避免点重叠\n",
    ")\n",
    "plt.legend(title='group', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f3ae2c-3b13-4c97-9501-a9b3d1e088da",
   "metadata": {},
   "source": [
    "## depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "a494a3db-5de3-4e8d-97de-0b47656e236f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>524</th>\n",
       "      <th>525</th>\n",
       "      <th>526</th>\n",
       "      <th>527</th>\n",
       "      <th>528</th>\n",
       "      <th>529</th>\n",
       "      <th>530</th>\n",
       "      <th>531</th>\n",
       "      <th>532</th>\n",
       "      <th>533</th>\n",
       "      <th>...</th>\n",
       "      <th>8216</th>\n",
       "      <th>8217</th>\n",
       "      <th>8218</th>\n",
       "      <th>8219</th>\n",
       "      <th>8220</th>\n",
       "      <th>8221</th>\n",
       "      <th>8222</th>\n",
       "      <th>8223</th>\n",
       "      <th>8224</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c1941</th>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1947</th>\n",
       "      <td>38.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1953</th>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1957</th>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1959</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2321</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2324</th>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2341</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2356</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2359</th>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4990 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        524   525   526   527   528   529   530   531   532   533  ...  8216  \\\n",
       "c1941  20.0  22.0  22.0  22.0  23.0  24.0  23.0  25.0  24.0  26.0  ...  29.0   \n",
       "c1947  38.0  36.0  39.0  40.0  45.0  45.0  47.0  46.0  45.0  45.0  ...  34.0   \n",
       "c1953  37.0  35.0  36.0  40.0  41.0  40.0  38.0  41.0  41.0  39.0  ...  41.0   \n",
       "c1957  28.0  28.0  29.0  29.0  28.0  28.0  29.0  29.0  29.0  29.0  ...  31.0   \n",
       "c1959  38.0  38.0  43.0  42.0  43.0  44.0  43.0  46.0  45.0  47.0  ...  32.0   \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "c2321  14.0  15.0  17.0  17.0  17.0  16.0  17.0  17.0  17.0  17.0  ...   9.0   \n",
       "c2324  17.0  18.0  18.0  18.0  16.0  18.0  18.0  20.0  20.0  20.0  ...  35.0   \n",
       "c2341  11.0  12.0  12.0  11.0  13.0  13.0  13.0  14.0  14.0  13.0  ...  17.0   \n",
       "c2356  17.0  17.0  18.0  19.0  19.0  19.0  19.0  19.0  19.0  19.0  ...  11.0   \n",
       "c2359  21.0  21.0  23.0  24.0  25.0  24.0  25.0  27.0  27.0  27.0  ...  20.0   \n",
       "\n",
       "       8217  8218  8219  8220  8221  8222  8223  8224 Category  \n",
       "c1941  31.0  32.0  32.0  32.0  33.0  33.0  33.0  33.0        A  \n",
       "c1947  36.0  37.0  35.0  40.0  38.0  40.0  40.0  40.0        A  \n",
       "c1953  41.0  43.0  43.0  42.0  38.0  46.0  44.0  43.0        A  \n",
       "c1957  30.0  31.0  31.0  31.0  31.0  30.0  34.0  33.0        A  \n",
       "c1959  33.0  34.0  35.0  35.0  35.0  34.0  35.0  35.0        A  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...      ...  \n",
       "c2321   9.0   8.0  10.0  10.0   9.0  10.0  10.0   9.0        A  \n",
       "c2324  36.0  36.0  37.0  37.0  37.0  37.0  36.0  37.0        A  \n",
       "c2341  16.0  17.0  16.0  17.0  17.0  16.0  17.0  17.0        A  \n",
       "c2356  11.0  11.0  11.0  11.0  11.0  11.0  11.0  11.0        A  \n",
       "c2359  21.0  20.0  21.0  21.0  20.0  21.0  21.0  21.0        A  \n",
       "\n",
       "[99 rows x 4990 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chd = merged_df[merged_df['avg'] > 20].T.iloc[2:-8,:]\n",
    "df_chd['Category'] = \"A\"\n",
    "df_chd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5fc55ab1-0740-4a48-9e7f-722a262ad992",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "position = 524\n",
    "real_position = merged_df.iloc[524,1]\n",
    "plt.figure(figsize=(4, 6))\n",
    "\n",
    "# 修改后的小提琴图（解决palette警告）\n",
    "sns.violinplot(\n",
    "    x='Category',      # 分类变量（x轴分组）\n",
    "    y=position,        # 数值变量（y轴分布）\n",
    "    data=df_chd,           # 数据源\n",
    "    hue='Category',    # 新增hue参数以配合palette\n",
    "    palette='Set2',    # 配色方案\n",
    "    legend=False,      # 避免重复图例\n",
    "    inner='quartile'   # 内部显示四分位数线\n",
    ")\n",
    "\n",
    "# 修改后的散点图（保持黑色显示）\n",
    "sns.stripplot(\n",
    "    x='Category',\n",
    "    y=position,\n",
    "    data=df_chd,\n",
    "    color='black',     # 直接指定颜色（覆盖palette）\n",
    "    size=4,\n",
    "    alpha=0.3,\n",
    "    jitter=True        # 避免点重叠\n",
    ")\n",
    "\n",
    "# 添加红色标记点（在Category=A的y=48处）\n",
    "plt.scatter(\n",
    "    x=0,               # Category=A的索引位置\n",
    "    y=48,              # 标记点的y值\n",
    "    color='red',       # 红色\n",
    "    s=100,             # 点大小\n",
    "    label='now',       # 图例标签\n",
    "    zorder=10          # 确保标记点在最上层\n",
    ")\n",
    "\n",
    "plt.title('Violin Plot with position depth of ' + str(real_position), fontsize=14)\n",
    "plt.xlabel('type', fontsize=12)\n",
    "plt.ylabel('data', fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.4)\n",
    "plt.legend()  # 显示图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a192fe-00c6-4e0d-a027-03937741bba2",
   "metadata": {},
   "source": [
    "# sample depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "680eae09-b85b-4990-8731-4efe052f703c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8139</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8140</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8141</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8142</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8143</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8144 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  SRR14724496  SRR14724497  SRR14724499  SRR14724500  \\\n",
       "0     chr6  32038100          1.0          0.0          0.0            1   \n",
       "1     chr6  32038101          1.0          0.0          0.0            1   \n",
       "2     chr6  32038102          1.0          0.0          0.0            1   \n",
       "3     chr6  32038103          1.0          0.0          0.0            1   \n",
       "4     chr6  32038104          1.0          0.0          0.0            1   \n",
       "...    ...       ...          ...          ...          ...          ...   \n",
       "8139  chr6  32038095          0.0          0.0          0.0            1   \n",
       "8140  chr6  32038096          0.0          0.0          0.0            1   \n",
       "8141  chr6  32038097          0.0          0.0          0.0            1   \n",
       "8142  chr6  32038098          0.0          0.0          0.0            1   \n",
       "8143  chr6  32038099          0.0          0.0          0.0            1   \n",
       "\n",
       "      SRR14724501  SRR14724502  \n",
       "0             0.0          0.0  \n",
       "1             0.0          0.0  \n",
       "2             1.0          0.0  \n",
       "3             0.0          0.0  \n",
       "4             1.0          0.0  \n",
       "...           ...          ...  \n",
       "8139          1.0          0.0  \n",
       "8140          1.0          0.0  \n",
       "8141          0.0          0.0  \n",
       "8142          1.0          0.0  \n",
       "8143          1.0          0.0  \n",
       "\n",
       "[8144 rows x 8 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dirListPath = '/lustre/home/acct-medfzx/medfzx-lkw/project/2rd_NGS_annovar_LD/result/WES_HG000X/bam'\n",
    "\n",
    "fileList = os.listdir(dirListPath)\n",
    "fileList = [x for x in fileList if x.startswith('SRR')]\n",
    "fileList.sort()\n",
    "\n",
    "dfs = []\n",
    "for fileDir in fileList[:]:\n",
    "    fullDepthPath = os.path.join(dirListPath, fileDir, fileDir + \".target.MQ20.CYP21A2.depth.tsv\") \n",
    "    df = pd.read_csv(fullDepthPath, header=None, names=[\"chr\", 'position', fileDir], sep='\\t')\n",
    "    df.set_index(['chr', 'position'], inplace=True)\n",
    "    dfs.append(df)\n",
    "\n",
    "merged_df_sample = pd.concat(dfs, axis=1).reset_index()\n",
    "merged_df_sample.fillna(0, inplace=True)\n",
    "merged_df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8825d61a-c754-4452-a5dc-0825579b69b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "avgMT = 10\n",
    "CHD_Df = merged_df[merged_df['avg'] > avgMT]\n",
    "Pos_avg_dict = CHD_Df.set_index('position')['avg'].to_dict()\n",
    "Pos_std_dict = CHD_Df.set_index('position')['std'].to_dict()\n",
    "Pos_LT1std_dict = CHD_Df.set_index('position')['LT_1std'].to_dict()\n",
    "Pos_LT2std_dict = CHD_Df.set_index('position')['LT_2std'].to_dict()\n",
    "Pos_LT3std_dict = CHD_Df.set_index('position')['LT_3std'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "84457dc1-d8d4-4257-91e8-3cf4946a7961",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8139</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8140</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8141</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8142</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8143</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8144 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  SRR14724496  SRR14724497  SRR14724499  SRR14724500  \\\n",
       "0     chr6  32038100          1.0          0.0          0.0            1   \n",
       "1     chr6  32038101          1.0          0.0          0.0            1   \n",
       "2     chr6  32038102          1.0          0.0          0.0            1   \n",
       "3     chr6  32038103          1.0          0.0          0.0            1   \n",
       "4     chr6  32038104          1.0          0.0          0.0            1   \n",
       "...    ...       ...          ...          ...          ...          ...   \n",
       "8139  chr6  32038095          0.0          0.0          0.0            1   \n",
       "8140  chr6  32038096          0.0          0.0          0.0            1   \n",
       "8141  chr6  32038097          0.0          0.0          0.0            1   \n",
       "8142  chr6  32038098          0.0          0.0          0.0            1   \n",
       "8143  chr6  32038099          0.0          0.0          0.0            1   \n",
       "\n",
       "      SRR14724501  SRR14724502  \n",
       "0             0.0          0.0  \n",
       "1             0.0          0.0  \n",
       "2             1.0          0.0  \n",
       "3             0.0          0.0  \n",
       "4             1.0          0.0  \n",
       "...           ...          ...  \n",
       "8139          1.0          0.0  \n",
       "8140          1.0          0.0  \n",
       "8141          0.0          0.0  \n",
       "8142          1.0          0.0  \n",
       "8143          1.0          0.0  \n",
       "\n",
       "[8144 rows x 8 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6da4b19a-daa3-47b9-862a-554eeccdfe33",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def judge_conversion2(row):\n",
    "    \"\"\"根据 position 列的值返回多个统计量\"\"\"\n",
    "    if row['position'] not in Pos_avg_dict.keys():\n",
    "        return None, None, None, None, None  # 返回空值（或根据需求调整）\n",
    "    else:\n",
    "        avg = Pos_avg_dict[row['position']]\n",
    "        std = Pos_std_dict[row['position']]\n",
    "        LT1stdNum = Pos_LT1std_dict[row['position']]\n",
    "        LT2stdNum = Pos_LT2std_dict[row['position']]\n",
    "        LT3stdNum = Pos_LT3std_dict[row['position']]\n",
    "        return avg, std, LT1stdNum, LT2stdNum, LT3stdNum\n",
    "\n",
    "# 使用 apply 并展开为多列\n",
    "merged_df_sample[['avg', 'std', 'LT1stdNum', 'LT2stdNum', 'LT3stdNum']] = merged_df_sample.apply(\n",
    "    judge_conversion2, \n",
    "    axis=1, \n",
    "    result_type='expand'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "abbe0535-225f-4b84-9841-911f140887bb",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038377</td>\n",
       "      <td>55.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.070707</td>\n",
       "      <td>5.213139</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038378</td>\n",
       "      <td>57.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.363636</td>\n",
       "      <td>5.424327</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038379</td>\n",
       "      <td>57.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.949495</td>\n",
       "      <td>5.645010</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038380</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>42</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.979798</td>\n",
       "      <td>5.572261</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038381</td>\n",
       "      <td>56.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>42</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.181818</td>\n",
       "      <td>5.592859</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8064</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046164</td>\n",
       "      <td>127.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>127</td>\n",
       "      <td>106.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>21.565657</td>\n",
       "      <td>8.019121</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8065</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046165</td>\n",
       "      <td>128.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>126</td>\n",
       "      <td>104.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>21.222222</td>\n",
       "      <td>7.882413</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8066</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046166</td>\n",
       "      <td>130.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>127</td>\n",
       "      <td>104.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>21.878788</td>\n",
       "      <td>8.286868</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8067</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046167</td>\n",
       "      <td>127.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>124</td>\n",
       "      <td>102.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>22.060606</td>\n",
       "      <td>8.232503</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32046168</td>\n",
       "      <td>128.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>126</td>\n",
       "      <td>102.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>21.979798</td>\n",
       "      <td>8.114060</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6320 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  SRR14724496  SRR14724497  SRR14724499  SRR14724500  \\\n",
       "277   chr6  32038377         55.0         69.0         50.0           40   \n",
       "278   chr6  32038378         57.0         69.0         52.0           40   \n",
       "279   chr6  32038379         57.0         71.0         51.0           41   \n",
       "280   chr6  32038380         56.0         70.0         51.0           42   \n",
       "281   chr6  32038381         56.0         71.0         52.0           42   \n",
       "...    ...       ...          ...          ...          ...          ...   \n",
       "8064  chr6  32046164        127.0        155.0        163.0          127   \n",
       "8065  chr6  32046165        128.0        156.0        161.0          126   \n",
       "8066  chr6  32046166        130.0        152.0        160.0          127   \n",
       "8067  chr6  32046167        127.0        153.0        157.0          124   \n",
       "8068  chr6  32046168        128.0        151.0        157.0          126   \n",
       "\n",
       "      SRR14724501  SRR14724502        avg       std  LT1stdNum  LT2stdNum  \\\n",
       "277          31.0         24.0  10.070707  5.213139       12.0        0.0   \n",
       "278          33.0         25.0  10.363636  5.424327        9.0        0.0   \n",
       "279          33.0         25.0  10.949495  5.645010       17.0        0.0   \n",
       "280          33.0         25.0  10.979798  5.572261       16.0        0.0   \n",
       "281          33.0         25.0  11.181818  5.592859       14.0        0.0   \n",
       "...           ...          ...        ...       ...        ...        ...   \n",
       "8064        106.0        117.0  21.565657  8.019121       18.0        0.0   \n",
       "8065        104.0        120.0  21.222222  7.882413       18.0        0.0   \n",
       "8066        104.0        115.0  21.878788  8.286868       16.0        0.0   \n",
       "8067        102.0        119.0  22.060606  8.232503       14.0        0.0   \n",
       "8068        102.0        118.0  21.979798  8.114060       14.0        0.0   \n",
       "\n",
       "      LT3stdNum  \n",
       "277         0.0  \n",
       "278         0.0  \n",
       "279         0.0  \n",
       "280         0.0  \n",
       "281         0.0  \n",
       "...         ...  \n",
       "8064        0.0  \n",
       "8065        0.0  \n",
       "8066        0.0  \n",
       "8067        0.0  \n",
       "8068        0.0  \n",
       "\n",
       "[6320 rows x 13 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_sample = merged_df_sample[~merged_df_sample['avg'].isna()]\n",
    "df_filtered_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f0dfa254-7566-4443-bdb3-e47c8734a9bf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "target_positions = [32039143, 32041122, 32041503, 32041598, 32041678, 32042418,32042505]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8bf78d82-5e22-42d9-8fde-392318289ee0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [chr, position, SRR14724496, SRR14724497, SRR14724499, SRR14724500, SRR14724501, SRR14724502, avg, std, LT1stdNum, LT2stdNum, LT3stdNum]\n",
       "Index: []"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_sample[df_filtered_sample['position'] == 32041125]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "a1907632-c688-4c75-95f4-24659baf588d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039143</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.626263</td>\n",
       "      <td>16.084741</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3403</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041503</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.848485</td>\n",
       "      <td>9.973058</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4318</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042418</td>\n",
       "      <td>124.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>220</td>\n",
       "      <td>67.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>57.919192</td>\n",
       "      <td>20.282189</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4405</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042505</td>\n",
       "      <td>131.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>286</td>\n",
       "      <td>92.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>43.454545</td>\n",
       "      <td>18.118171</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  SRR14724496  SRR14724497  SRR14724499  SRR14724500  \\\n",
       "1043  chr6  32039143         44.0         43.0         39.0           70   \n",
       "3403  chr6  32041503         19.0         18.0         22.0           27   \n",
       "4318  chr6  32042418        124.0        107.0        134.0          220   \n",
       "4405  chr6  32042505        131.0        155.0        163.0          286   \n",
       "\n",
       "      SRR14724501  SRR14724502        avg        std  LT1stdNum  LT2stdNum  \\\n",
       "1043         25.0         47.0  34.626263  16.084741       18.0        0.0   \n",
       "3403         17.0         27.0  25.848485   9.973058       13.0        0.0   \n",
       "4318         67.0        134.0  57.919192  20.282189       17.0        0.0   \n",
       "4405         92.0        165.0  43.454545  18.118171       11.0        0.0   \n",
       "\n",
       "      LT3stdNum  \n",
       "1043        0.0  \n",
       "3403        0.0  \n",
       "4318        0.0  \n",
       "4405        0.0  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_positions = [32039143, 32041122, 32041503, 32041598, 32041678, 32042418,32042505]\n",
    "filtered_rows = df_filtered_sample[df_filtered_sample['position'].isin(target_positions)]\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "18c3b76e-6f7b-4232-aed3-8ad16084df4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>SRR14724496</th>\n",
       "      <th>SRR14724497</th>\n",
       "      <th>SRR14724499</th>\n",
       "      <th>SRR14724500</th>\n",
       "      <th>SRR14724501</th>\n",
       "      <th>SRR14724502</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041122</td>\n",
       "      <td>60.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>136</td>\n",
       "      <td>24.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  SRR14724496  SRR14724497  SRR14724499  SRR14724500  \\\n",
       "3022  chr6  32041122         60.0         73.0         64.0          136   \n",
       "\n",
       "      SRR14724501  SRR14724502  avg  std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "3022         24.0         70.0  NaN  NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_sample[merged_df_sample['position'] == 32041122]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "ed20a190-06e4-4537-b0a1-279daaeca54c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>c1941</th>\n",
       "      <th>c1947</th>\n",
       "      <th>c1953</th>\n",
       "      <th>c1957</th>\n",
       "      <th>c1959</th>\n",
       "      <th>c1965</th>\n",
       "      <th>c1978</th>\n",
       "      <th>c1989</th>\n",
       "      <th>...</th>\n",
       "      <th>c2356</th>\n",
       "      <th>c2359</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>1x_std_range</th>\n",
       "      <th>2x_std_range</th>\n",
       "      <th>3x_std_range</th>\n",
       "      <th>LT_1std</th>\n",
       "      <th>LT_2std</th>\n",
       "      <th>LT_3std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041122</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.767677</td>\n",
       "      <td>5.937092</td>\n",
       "      <td>(2.83, 14.7)</td>\n",
       "      <td>(-3.11, 20.64)</td>\n",
       "      <td>(-9.04, 26.58)</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  c1941  c1947  c1953  c1957  c1959  c1965  c1978  c1989  \\\n",
       "3227  chr6  32041122    3.0    3.0   24.0    7.0   12.0   10.0   17.0    7.0   \n",
       "\n",
       "      ...  c2356  c2359       avg       std  1x_std_range    2x_std_range  \\\n",
       "3227  ...   11.0    8.0  8.767677  5.937092  (2.83, 14.7)  (-3.11, 20.64)   \n",
       "\n",
       "        3x_std_range  LT_1std  LT_2std  LT_3std  \n",
       "3227  (-9.04, 26.58)        8        0        0  \n",
       "\n",
       "[1 rows x 109 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['position'] == 32041122]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0defc116-e7a9-432e-9e17-d7012f5de2eb",
   "metadata": {},
   "source": [
    "# 3rd 2rd both sample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "21a97165-82bb-4972-8182-799a9a6b340f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NGS_11298_BW_C', 'NGS_3403_BW_C', 'NGS_5416_BW_C', 'NGS_8239_BW_C', 'NGS_9412_BW_C', 'NGS_9562_BW_C', 'NGS_9773_BW_C']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038136</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038137</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038138</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038139</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8194</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8195</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8196</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8197</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8198</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8199 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  NGS_11298_BW_C  NGS_3403_BW_C  NGS_5416_BW_C  \\\n",
       "0     chr6  32038136             2.0            2.0            0.0   \n",
       "1     chr6  32038137             2.0            2.0            0.0   \n",
       "2     chr6  32038138             2.0            2.0            0.0   \n",
       "3     chr6  32038139             2.0            2.0            0.0   \n",
       "4     chr6  32038140             2.0            2.0            0.0   \n",
       "...    ...       ...             ...            ...            ...   \n",
       "8194  chr6  32038070             0.0            0.0            0.0   \n",
       "8195  chr6  32038071             0.0            0.0            0.0   \n",
       "8196  chr6  32038072             0.0            0.0            0.0   \n",
       "8197  chr6  32038073             0.0            0.0            0.0   \n",
       "8198  chr6  32038074             0.0            0.0            0.0   \n",
       "\n",
       "      NGS_8239_BW_C  NGS_9412_BW_C  NGS_9562_BW_C  NGS_9773_BW_C  \n",
       "0               0.0            1.0            5.0            4.0  \n",
       "1               0.0            1.0            5.0            4.0  \n",
       "2               0.0            1.0            4.0            4.0  \n",
       "3               0.0            1.0            5.0            3.0  \n",
       "4               0.0            1.0            4.0            4.0  \n",
       "...             ...            ...            ...            ...  \n",
       "8194            3.0            0.0            2.0            0.0  \n",
       "8195            3.0            0.0            2.0            0.0  \n",
       "8196            3.0            0.0            2.0            0.0  \n",
       "8197            3.0            0.0            2.0            0.0  \n",
       "8198            3.0            0.0            2.0            0.0  \n",
       "\n",
       "[8199 rows x 9 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path_2rd_3rd_dir = \"/lustre/home/acct-medfzx/medfzx-lkw/project/2rd_NGS_annovar_LD/result/Patient_WES_have_hifi/bam\"\n",
    "fileList = os.listdir(Path_2rd_3rd_dir)\n",
    "fileList = [x for x in fileList if x.startswith('NGS')]\n",
    "notIn = ['NGS_13747_BW_C', 'NGS_14845_BW_C', 'NGS_14847_BW_C', 'NGS_16106_BW_C']  # , 'NGS_11298_BW_C'\n",
    "fileList = [x for x in fileList if x not in notIn]\n",
    "fileList.sort()\n",
    "print(fileList)\n",
    "dfs = []\n",
    "for fileDir in fileList[:]:\n",
    "    fullDepthPath = os.path.join(Path_2rd_3rd_dir, fileDir, fileDir + \".target.MQ20.CYP21A2.depth.tsv\") \n",
    "    df = pd.read_csv(fullDepthPath, header=None, names=[\"chr\", 'position', fileDir], sep='\\t')\n",
    "    df.set_index(['chr', 'position'], inplace=True)\n",
    "    dfs.append(df)\n",
    "\n",
    "merged_df_23 = pd.concat(dfs, axis=1).reset_index()\n",
    "merged_df_23.fillna(0, inplace=True)\n",
    "merged_df_23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c16c481-e210-4b90-bd73-cd4cd49e30c0",
   "metadata": {},
   "source": [
    "## 制作标准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "8eea5ca0-4959-4890-a9a3-20ac6433a362",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2147292/3755882.py:12: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  merged_df_23[['1x_std_range', '2x_std_range', '3x_std_range']] = merged_df_23[['1x_std_range', '2x_std_range', '3x_std_range']].applymap(ast.literal_eval)\n"
     ]
    }
   ],
   "source": [
    "# 计算各行的均值和标准差（从第3列开始）\n",
    "merged_df_23['avg'] = merged_df_23.iloc[:, 2:].mean(axis=1)\n",
    "merged_df_23['std'] = merged_df_23.iloc[:, 2:].std(axis=1)\n",
    "\n",
    "# 生成1x、2x、3x标准差范围字符串\n",
    "merged_df_23['1x_std_range'] = '(' + (merged_df_23['avg'] - merged_df_23['std']).round(2).astype(str) + ', ' + (merged_df_23['avg'] + merged_df_23['std']).round(2).astype(str) + ')'\n",
    "merged_df_23['2x_std_range'] = '(' + (merged_df_23['avg'] - 2 * merged_df_23['std']).round(2).astype(str) + ', ' + (merged_df_23['avg'] + 2 * merged_df_23['std']).round(2).astype(str) + ')'\n",
    "merged_df_23['3x_std_range'] = '(' + (merged_df_23['avg'] - 3 * merged_df_23['std']).round(2).astype(str) + ', ' + (merged_df_23['avg'] + 3 * merged_df_23['std']).round(2).astype(str) + ')'\n",
    "\n",
    "# 将字符串范围转换为元组类型\n",
    "import ast\n",
    "merged_df_23[['1x_std_range', '2x_std_range', '3x_std_range']] = merged_df_23[['1x_std_range', '2x_std_range', '3x_std_range']].applymap(ast.literal_eval)\n",
    "\n",
    "target_columns = merged_df_23.columns[2:-5]\n",
    "std1 = merged_df_23['avg'] - merged_df_23['std']\n",
    "std2 = merged_df_23['avg'] - 2*merged_df_23['std']\n",
    "std3 = merged_df_23['avg'] - 3*merged_df_23['std']\n",
    "\n",
    "# 统计满足条件的列数\n",
    "merged_df_23['LT_1std'] = merged_df_23[target_columns].lt(std1, axis=0).sum(axis=1)\n",
    "merged_df_23['LT_2std'] = merged_df_23[target_columns].lt(std2, axis=0).sum(axis=1)\n",
    "merged_df_23['LT_3std'] = merged_df_23[target_columns].lt(std3, axis=0).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56fc5b1-12ce-44b1-8d55-bc8239b92351",
   "metadata": {},
   "source": [
    "## 建立标准字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "7fba1f87-7ce8-4945-a223-e6160a2294cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "avgMT = 10\n",
    "df_23 = merged_df_23[merged_df_23['avg'] > avgMT]\n",
    "Pos_avg_dict = df_23.set_index('position')['avg'].to_dict()\n",
    "Pos_std_dict = df_23.set_index('position')['std'].to_dict()\n",
    "Pos_LT1std_dict = df_23.set_index('position')['LT_1std'].to_dict()\n",
    "Pos_LT2std_dict = df_23.set_index('position')['LT_2std'].to_dict()\n",
    "Pos_LT3std_dict = df_23.set_index('position')['LT_3std'].to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d79c020-fcb2-4f41-bcff-7463ebbe015c",
   "metadata": {},
   "source": [
    "## 嵌入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "6a792187-9ef8-40e1-845a-d6d5cb792218",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def judge_conversion2(row):\n",
    "    \"\"\"根据 position 列的值返回多个统计量\"\"\"\n",
    "    if row['position'] not in Pos_avg_dict.keys():\n",
    "        return None, None, None, None, None  # 返回空值（或根据需求调整）\n",
    "    else:\n",
    "        avg = Pos_avg_dict[row['position']]\n",
    "        std = Pos_std_dict[row['position']]\n",
    "        LT1stdNum = Pos_LT1std_dict[row['position']]\n",
    "        LT2stdNum = Pos_LT2std_dict[row['position']]\n",
    "        LT3stdNum = Pos_LT3std_dict[row['position']]\n",
    "        return avg, std, LT1stdNum, LT2stdNum, LT3stdNum\n",
    "\n",
    "# 使用 apply 并展开为多列\n",
    "merged_df_23[['avg', 'std', 'LT1stdNum', 'LT2stdNum', 'LT3stdNum']] = merged_df_23.apply(\n",
    "    judge_conversion2, \n",
    "    axis=1, \n",
    "    result_type='expand'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "f7016762-7e80-4912-8d3d-4b00bfddb969",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038558</td>\n",
       "      <td>108.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>55.784556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038559</td>\n",
       "      <td>107.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>102.833333</td>\n",
       "      <td>57.091788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038560</td>\n",
       "      <td>90.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>58.114829</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038561</td>\n",
       "      <td>90.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>55.388777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7469</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045605</td>\n",
       "      <td>193.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>236.166667</td>\n",
       "      <td>95.200519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7470</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045606</td>\n",
       "      <td>191.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>235.333333</td>\n",
       "      <td>96.063636</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7471</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045607</td>\n",
       "      <td>190.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>233.833333</td>\n",
       "      <td>95.612790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7472</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045608</td>\n",
       "      <td>188.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>235.166667</td>\n",
       "      <td>94.414718</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7473</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045609</td>\n",
       "      <td>188.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>233.500000</td>\n",
       "      <td>93.685200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6249 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  NGS_11298_BW_C  NGS_3403_BW_C  NGS_5416_BW_C  \\\n",
       "421   chr6  32038557           108.0          191.0           51.0   \n",
       "422   chr6  32038558           108.0          193.0           53.0   \n",
       "423   chr6  32038559           107.0          196.0           52.0   \n",
       "424   chr6  32038560            90.0          193.0           52.0   \n",
       "425   chr6  32038561            90.0          182.0           42.0   \n",
       "...    ...       ...             ...            ...            ...   \n",
       "7469  chr6  32045605           193.0          369.0          129.0   \n",
       "7470  chr6  32045606           191.0          371.0          128.0   \n",
       "7471  chr6  32045607           190.0          371.0          125.0   \n",
       "7472  chr6  32045608           188.0          369.0          129.0   \n",
       "7473  chr6  32045609           188.0          365.0          131.0   \n",
       "\n",
       "      NGS_8239_BW_C  NGS_9412_BW_C  NGS_9562_BW_C  NGS_9773_BW_C         avg  \\\n",
       "421           146.0          122.0           55.0           47.0  102.000000   \n",
       "422           149.0          120.0           55.0           45.0  102.500000   \n",
       "423           148.0          123.0           54.0           44.0  102.833333   \n",
       "424           146.0          118.0           54.0           31.0   99.000000   \n",
       "425           143.0          108.0           52.0           34.0   93.500000   \n",
       "...             ...            ...            ...            ...         ...   \n",
       "7469          362.0          176.0          219.0          162.0  236.166667   \n",
       "7470          361.0          175.0          217.0          160.0  235.333333   \n",
       "7471          356.0          172.0          217.0          162.0  233.833333   \n",
       "7472          358.0          177.0          217.0          161.0  235.166667   \n",
       "7473          358.0          174.0          213.0          160.0  233.500000   \n",
       "\n",
       "            std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "421   54.912051        1.0        0.0        0.0  \n",
       "422   55.784556        1.0        0.0        0.0  \n",
       "423   57.091788        1.0        0.0        0.0  \n",
       "424   58.114829        1.0        0.0        0.0  \n",
       "425   55.388777        1.0        0.0        0.0  \n",
       "...         ...        ...        ...        ...  \n",
       "7469  95.200519        1.0        0.0        0.0  \n",
       "7470  96.063636        1.0        0.0        0.0  \n",
       "7471  95.612790        1.0        0.0        0.0  \n",
       "7472  94.414718        1.0        0.0        0.0  \n",
       "7473  93.685200        1.0        0.0        0.0  \n",
       "\n",
       "[6249 rows x 14 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_sample = merged_df_23[~merged_df_23['avg'].isna()]\n",
    "df_filtered_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "deb5b404-22b5-4949-b7df-d3233084c00c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/lustre/home/acct-medfzx/medfzx-lkw/project/simulation/script\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "b72861c8-aa1c-475e-a6ae-d2d13a213482",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# exon 1  32038557\n",
    "# exon 2  32038800\n",
    "# exon 3  32039143\n",
    "# exon 4  32039425\n",
    "# exon 5  32039555\n",
    "# exon 6  32039801\n",
    "# exon 7  32040110\n",
    "# exon 8  32040421\n",
    "# exon 8-2 32040533\n",
    "# exon 9  32041111\n",
    "# exon 10 32041480\n",
    "# exon 40 32042500\n",
    "# exon 35 32043800\n",
    "# exon 32 32045150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "f6f06699-03fa-413c-bca4-ee66abea4f4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038800</td>\n",
       "      <td>42.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>20.516254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039143</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>23.283876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039425</td>\n",
       "      <td>62.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>21.786846</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039555</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>17.761538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039801</td>\n",
       "      <td>42.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>15.290702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040110</td>\n",
       "      <td>53.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.166667</td>\n",
       "      <td>22.615752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040421</td>\n",
       "      <td>38.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.166667</td>\n",
       "      <td>39.792866</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040533</td>\n",
       "      <td>73.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>66.512321</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041111</td>\n",
       "      <td>87.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>70.279798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041480</td>\n",
       "      <td>67.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>15.282525</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042500</td>\n",
       "      <td>326.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>130.783536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043800</td>\n",
       "      <td>266.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>182.166667</td>\n",
       "      <td>78.912223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045150</td>\n",
       "      <td>386.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>471.833333</td>\n",
       "      <td>174.054988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  NGS_11298_BW_C  NGS_3403_BW_C  NGS_5416_BW_C  \\\n",
       "421   chr6  32038557           108.0          191.0           51.0   \n",
       "664   chr6  32038800            42.0           86.0           25.0   \n",
       "1007  chr6  32039143             6.0           66.0           21.0   \n",
       "1289  chr6  32039425            62.0           85.0           29.0   \n",
       "1419  chr6  32039555            20.0           70.0           31.0   \n",
       "1665  chr6  32039801            42.0           62.0           25.0   \n",
       "1974  chr6  32040110            53.0           66.0           30.0   \n",
       "2285  chr6  32040421            38.0           92.0           25.0   \n",
       "2397  chr6  32040533            73.0          187.0           23.0   \n",
       "2975  chr6  32041111            87.0          167.0           48.0   \n",
       "3344  chr6  32041480            67.0           43.0           34.0   \n",
       "4364  chr6  32042500           326.0          250.0          103.0   \n",
       "5664  chr6  32043800           266.0          137.0           60.0   \n",
       "7014  chr6  32045150           386.0          700.0          306.0   \n",
       "\n",
       "      NGS_8239_BW_C  NGS_9412_BW_C  NGS_9562_BW_C  NGS_9773_BW_C         avg  \\\n",
       "421           146.0          122.0           55.0           47.0  102.000000   \n",
       "664            76.0           57.0           45.0           44.0   55.500000   \n",
       "1007           96.0           62.0           40.0           50.0   55.833333   \n",
       "1289           67.0           33.0           44.0           78.0   56.000000   \n",
       "1419           65.0           51.0           21.0           37.0   45.833333   \n",
       "1665           72.0           51.0           39.0           44.0   48.833333   \n",
       "1974          102.0           68.0           65.0           90.0   70.166667   \n",
       "2285          138.0           52.0           24.0           66.0   66.166667   \n",
       "2397          196.0          103.0           38.0           87.0  105.666667   \n",
       "2975          263.0           81.0           99.0          107.0  127.500000   \n",
       "3344           76.0           50.0           35.0           64.0   50.333333   \n",
       "4364          518.0          178.0          190.0          225.0  244.000000   \n",
       "5664          321.0          168.0          201.0          206.0  182.166667   \n",
       "7014          722.0          431.0          313.0          359.0  471.833333   \n",
       "\n",
       "             std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "421    54.912051        1.0        0.0        0.0  \n",
       "664    20.516254        1.0        0.0        0.0  \n",
       "1007   23.283876        1.0        0.0        0.0  \n",
       "1289   21.786846        2.0        0.0        0.0  \n",
       "1419   17.761538        1.0        0.0        0.0  \n",
       "1665   15.290702        1.0        0.0        0.0  \n",
       "1974   22.615752        1.0        0.0        0.0  \n",
       "2285   39.792866        2.0        0.0        0.0  \n",
       "2397   66.512321        2.0        0.0        0.0  \n",
       "2975   70.279798        1.0        0.0        0.0  \n",
       "3344   15.282525        2.0        0.0        0.0  \n",
       "4364  130.783536        1.0        0.0        0.0  \n",
       "5664   78.912223        1.0        0.0        0.0  \n",
       "7014  174.054988        0.0        0.0        0.0  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_positions = [32038557, 32038800, 32039143, 32039425, 32039555, 32039801, 32040110, 32040421, 32040533, 32041111, 32041480, 32042500, 32043800, 32045150]\n",
    "filtered_rows = df_filtered_sample[df_filtered_sample['position'].isin(target_positions)]\n",
    "print(filtered_rows.shape)\n",
    "filtered_rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "66001f36-cbb6-4777-b8cc-89845e4bbf95",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chr  position  NGS_11298_BW_C  NGS_3403_BW_C  NGS_5416_BW_C  \\\n",
       "421  chr6  32038557           108.0          191.0           51.0   \n",
       "\n",
       "     NGS_8239_BW_C  NGS_9412_BW_C  NGS_9562_BW_C  NGS_9773_BW_C    avg  \\\n",
       "421          146.0          122.0           55.0           47.0  102.0   \n",
       "\n",
       "           std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "421  54.912051        1.0        0.0        0.0  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_23[merged_df_23['position'] == 32038557]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1de927-29f7-4c44-8ca1-d478b6f54f3c",
   "metadata": {},
   "source": [
    "## 范围判断  std 1 2 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "7f5c65e5-f118-47dd-a137-01c29fe5aaa9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>stdRange</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.109266</td>\n",
       "      <td>191.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038800</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.658015</td>\n",
       "      <td>86.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>20.516254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039143</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.140251</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>23.283876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039425</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.275396</td>\n",
       "      <td>85.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>21.786846</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039555</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-1.454454</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>17.761538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039801</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.446895</td>\n",
       "      <td>62.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>15.290702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040110</td>\n",
       "      <td>53.0</td>\n",
       "      <td>-0.759058</td>\n",
       "      <td>66.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.166667</td>\n",
       "      <td>22.615752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040421</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.707832</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.166667</td>\n",
       "      <td>39.792866</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040533</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-0.491137</td>\n",
       "      <td>187.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>66.512321</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041111</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-0.576268</td>\n",
       "      <td>167.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>70.279798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041480</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.090570</td>\n",
       "      <td>43.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>15.282525</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042500</td>\n",
       "      <td>326.0</td>\n",
       "      <td>0.626990</td>\n",
       "      <td>250.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>130.783536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043800</td>\n",
       "      <td>266.0</td>\n",
       "      <td>1.062362</td>\n",
       "      <td>137.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>182.166667</td>\n",
       "      <td>78.912223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045150</td>\n",
       "      <td>386.0</td>\n",
       "      <td>-0.493139</td>\n",
       "      <td>700.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>471.833333</td>\n",
       "      <td>174.054988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  NGS_11298_BW_C  stdRange  NGS_3403_BW_C  NGS_5416_BW_C  \\\n",
       "421   chr6  32038557           108.0  0.109266          191.0           51.0   \n",
       "664   chr6  32038800            42.0 -0.658015           86.0           25.0   \n",
       "1007  chr6  32039143             6.0 -2.140251           66.0           21.0   \n",
       "1289  chr6  32039425            62.0  0.275396           85.0           29.0   \n",
       "1419  chr6  32039555            20.0 -1.454454           70.0           31.0   \n",
       "1665  chr6  32039801            42.0 -0.446895           62.0           25.0   \n",
       "1974  chr6  32040110            53.0 -0.759058           66.0           30.0   \n",
       "2285  chr6  32040421            38.0 -0.707832           92.0           25.0   \n",
       "2397  chr6  32040533            73.0 -0.491137          187.0           23.0   \n",
       "2975  chr6  32041111            87.0 -0.576268          167.0           48.0   \n",
       "3344  chr6  32041480            67.0  1.090570           43.0           34.0   \n",
       "4364  chr6  32042500           326.0  0.626990          250.0          103.0   \n",
       "5664  chr6  32043800           266.0  1.062362          137.0           60.0   \n",
       "7014  chr6  32045150           386.0 -0.493139          700.0          306.0   \n",
       "\n",
       "      NGS_8239_BW_C  NGS_9412_BW_C  NGS_9562_BW_C  NGS_9773_BW_C         avg  \\\n",
       "421           146.0          122.0           55.0           47.0  102.000000   \n",
       "664            76.0           57.0           45.0           44.0   55.500000   \n",
       "1007           96.0           62.0           40.0           50.0   55.833333   \n",
       "1289           67.0           33.0           44.0           78.0   56.000000   \n",
       "1419           65.0           51.0           21.0           37.0   45.833333   \n",
       "1665           72.0           51.0           39.0           44.0   48.833333   \n",
       "1974          102.0           68.0           65.0           90.0   70.166667   \n",
       "2285          138.0           52.0           24.0           66.0   66.166667   \n",
       "2397          196.0          103.0           38.0           87.0  105.666667   \n",
       "2975          263.0           81.0           99.0          107.0  127.500000   \n",
       "3344           76.0           50.0           35.0           64.0   50.333333   \n",
       "4364          518.0          178.0          190.0          225.0  244.000000   \n",
       "5664          321.0          168.0          201.0          206.0  182.166667   \n",
       "7014          722.0          431.0          313.0          359.0  471.833333   \n",
       "\n",
       "             std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "421    54.912051        1.0        0.0        0.0  \n",
       "664    20.516254        1.0        0.0        0.0  \n",
       "1007   23.283876        1.0        0.0        0.0  \n",
       "1289   21.786846        2.0        0.0        0.0  \n",
       "1419   17.761538        1.0        0.0        0.0  \n",
       "1665   15.290702        1.0        0.0        0.0  \n",
       "1974   22.615752        1.0        0.0        0.0  \n",
       "2285   39.792866        2.0        0.0        0.0  \n",
       "2397   66.512321        2.0        0.0        0.0  \n",
       "2975   70.279798        1.0        0.0        0.0  \n",
       "3344   15.282525        2.0        0.0        0.0  \n",
       "4364  130.783536        1.0        0.0        0.0  \n",
       "5664   78.912223        1.0        0.0        0.0  \n",
       "7014  174.054988        0.0        0.0        0.0  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_rows.insert(3, 'stdRange', (filtered_rows['NGS_11298_BW_C'] - filtered_rows['avg']) / filtered_rows['std'])\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09ff252c-0e87-4e36-937f-166abf9cfaa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exon 1  32038557\n",
    "# exon 2  32038800\n",
    "# exon 3  32039143\n",
    "# exon 4  32039425\n",
    "# exon 5  32039555\n",
    "# exon 6  32039801\n",
    "# exon 7  32040110\n",
    "# exon 8  32040421\n",
    "# exon 8-2 32040533\n",
    "# exon 9  32041111\n",
    "# exon 10 32041480\n",
    "# exon 40 32042500\n",
    "# exon 35 32043800\n",
    "# exon 32 32045150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "443aa604-d577-4245-8e9e-50a60380aee6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del filtered_rows['stdRange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "a657c0bc-564c-413b-957b-96659f3d5bb0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>11298</th>\n",
       "      <th>stdRange</th>\n",
       "      <th>3403</th>\n",
       "      <th>stdRange_3403</th>\n",
       "      <th>5416</th>\n",
       "      <th>stdRange_5416</th>\n",
       "      <th>8239</th>\n",
       "      <th>stdRange_9412</th>\n",
       "      <th>9412</th>\n",
       "      <th>stdRange_9562</th>\n",
       "      <th>9562</th>\n",
       "      <th>stdRange_9773</th>\n",
       "      <th>9773</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.109266</td>\n",
       "      <td>191.0</td>\n",
       "      <td>1.620774</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.928758</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.364219</td>\n",
       "      <td>122.0</td>\n",
       "      <td>-0.855914</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-1.001602</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038800</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.658015</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.486626</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.486626</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.073113</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-0.511789</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.560531</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>20.516254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039143</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.140251</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.436640</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-1.496028</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.264847</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-0.680013</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.250531</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>23.283876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039425</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.275396</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.331078</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-1.239280</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-1.055683</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-0.550791</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.009784</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>21.786846</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039555</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-1.454454</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.360618</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-0.835138</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.290891</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-1.398152</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.497329</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>17.761538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039801</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.446895</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.861090</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.558681</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.141698</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.643092</td>\n",
       "      <td>39.0</td>\n",
       "      <td>-0.316096</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>15.290702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040110</td>\n",
       "      <td>53.0</td>\n",
       "      <td>-0.759058</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-0.184237</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-1.776048</td>\n",
       "      <td>102.0</td>\n",
       "      <td>-0.095803</td>\n",
       "      <td>68.0</td>\n",
       "      <td>-0.228454</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.876970</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.166667</td>\n",
       "      <td>22.615752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040421</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.707832</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.649195</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.034524</td>\n",
       "      <td>138.0</td>\n",
       "      <td>-0.356010</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-1.059654</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.004188</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.166667</td>\n",
       "      <td>39.792866</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-2</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040533</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-0.491137</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.222831</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-1.242877</td>\n",
       "      <td>196.0</td>\n",
       "      <td>-0.040093</td>\n",
       "      <td>103.0</td>\n",
       "      <td>-1.017355</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.280650</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>66.512321</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041111</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-0.576268</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.562039</td>\n",
       "      <td>48.0</td>\n",
       "      <td>-1.131193</td>\n",
       "      <td>263.0</td>\n",
       "      <td>-0.661641</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-0.405522</td>\n",
       "      <td>99.0</td>\n",
       "      <td>-0.291691</td>\n",
       "      <td>107.0</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>70.279798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041480</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.090570</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-0.479851</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1.068759</td>\n",
       "      <td>76.0</td>\n",
       "      <td>-0.021811</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-1.003325</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.894268</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>15.282525</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042500</td>\n",
       "      <td>326.0</td>\n",
       "      <td>0.626990</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.045877</td>\n",
       "      <td>103.0</td>\n",
       "      <td>-1.078117</td>\n",
       "      <td>518.0</td>\n",
       "      <td>-0.504651</td>\n",
       "      <td>178.0</td>\n",
       "      <td>-0.412896</td>\n",
       "      <td>190.0</td>\n",
       "      <td>-0.145278</td>\n",
       "      <td>225.0</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>130.783536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043800</td>\n",
       "      <td>266.0</td>\n",
       "      <td>1.062362</td>\n",
       "      <td>137.0</td>\n",
       "      <td>-0.572366</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-1.548134</td>\n",
       "      <td>321.0</td>\n",
       "      <td>-0.179524</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.238662</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0.302023</td>\n",
       "      <td>206.0</td>\n",
       "      <td>182.166667</td>\n",
       "      <td>78.912223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045150</td>\n",
       "      <td>386.0</td>\n",
       "      <td>-0.493139</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.310888</td>\n",
       "      <td>306.0</td>\n",
       "      <td>-0.952764</td>\n",
       "      <td>722.0</td>\n",
       "      <td>-0.234600</td>\n",
       "      <td>431.0</td>\n",
       "      <td>-0.912547</td>\n",
       "      <td>313.0</td>\n",
       "      <td>-0.648263</td>\n",
       "      <td>359.0</td>\n",
       "      <td>471.833333</td>\n",
       "      <td>174.054988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chr  position  11298  stdRange   3403  stdRange_3403   5416  \\\n",
       "1    chr6  32038557  108.0  0.109266  191.0       1.620774   51.0   \n",
       "2    chr6  32038800   42.0 -0.658015   86.0       1.486626   25.0   \n",
       "3    chr6  32039143    6.0 -2.140251   66.0       0.436640   21.0   \n",
       "4    chr6  32039425   62.0  0.275396   85.0       1.331078   29.0   \n",
       "5    chr6  32039555   20.0 -1.454454   70.0       1.360618   31.0   \n",
       "6    chr6  32039801   42.0 -0.446895   62.0       0.861090   25.0   \n",
       "7    chr6  32040110   53.0 -0.759058   66.0      -0.184237   30.0   \n",
       "8    chr6  32040421   38.0 -0.707832   92.0       0.649195   25.0   \n",
       "8-2  chr6  32040533   73.0 -0.491137  187.0       1.222831   23.0   \n",
       "9    chr6  32041111   87.0 -0.576268  167.0       0.562039   48.0   \n",
       "10   chr6  32041480   67.0  1.090570   43.0      -0.479851   34.0   \n",
       "40   chr6  32042500  326.0  0.626990  250.0       0.045877  103.0   \n",
       "35   chr6  32043800  266.0  1.062362  137.0      -0.572366   60.0   \n",
       "32   chr6  32045150  386.0 -0.493139  700.0       1.310888  306.0   \n",
       "\n",
       "     stdRange_5416   8239  stdRange_9412   9412  stdRange_9562   9562  \\\n",
       "1        -0.928758  146.0       0.364219  122.0      -0.855914   55.0   \n",
       "2        -1.486626   76.0       0.073113   57.0      -0.511789   45.0   \n",
       "3        -1.496028   96.0       0.264847   62.0      -0.680013   40.0   \n",
       "4        -1.239280   67.0      -1.055683   33.0      -0.550791   44.0   \n",
       "5        -0.835138   65.0       0.290891   51.0      -1.398152   21.0   \n",
       "6        -1.558681   72.0       0.141698   51.0      -0.643092   39.0   \n",
       "7        -1.776048  102.0      -0.095803   68.0      -0.228454   65.0   \n",
       "8        -1.034524  138.0      -0.356010   52.0      -1.059654   24.0   \n",
       "8-2      -1.242877  196.0      -0.040093  103.0      -1.017355   38.0   \n",
       "9        -1.131193  263.0      -0.661641   81.0      -0.405522   99.0   \n",
       "10       -1.068759   76.0      -0.021811   50.0      -1.003325   35.0   \n",
       "40       -1.078117  518.0      -0.504651  178.0      -0.412896  190.0   \n",
       "35       -1.548134  321.0      -0.179524  168.0       0.238662  201.0   \n",
       "32       -0.952764  722.0      -0.234600  431.0      -0.912547  313.0   \n",
       "\n",
       "     stdRange_9773   9773         avg         std  LT1stdNum  LT2stdNum  \\\n",
       "1        -1.001602   47.0  102.000000   54.912051        1.0        0.0   \n",
       "2        -0.560531   44.0   55.500000   20.516254        1.0        0.0   \n",
       "3        -0.250531   50.0   55.833333   23.283876        1.0        0.0   \n",
       "4         1.009784   78.0   56.000000   21.786846        2.0        0.0   \n",
       "5        -0.497329   37.0   45.833333   17.761538        1.0        0.0   \n",
       "6        -0.316096   44.0   48.833333   15.290702        1.0        0.0   \n",
       "7         0.876970   90.0   70.166667   22.615752        1.0        0.0   \n",
       "8        -0.004188   66.0   66.166667   39.792866        2.0        0.0   \n",
       "8-2      -0.280650   87.0  105.666667   66.512321        2.0        0.0   \n",
       "9        -0.291691  107.0  127.500000   70.279798        1.0        0.0   \n",
       "10        0.894268   64.0   50.333333   15.282525        2.0        0.0   \n",
       "40       -0.145278  225.0  244.000000  130.783536        1.0        0.0   \n",
       "35        0.302023  206.0  182.166667   78.912223        1.0        0.0   \n",
       "32       -0.648263  359.0  471.833333  174.054988        0.0        0.0   \n",
       "\n",
       "     LT3stdNum  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "5          0.0  \n",
       "6          0.0  \n",
       "7          0.0  \n",
       "8          0.0  \n",
       "8-2        0.0  \n",
       "9          0.0  \n",
       "10         0.0  \n",
       "40         0.0  \n",
       "35         0.0  \n",
       "32         0.0  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtered_rows.rename(columns={'NGS_11298_BW_C': '11298', 'NGS_3403_BW_C': '3403', 'NGS_5416_BW_C': '5416', 'NGS_8239_BW_C': '8239', 'NGS_9412_BW_C':'9412', 'NGS_9562_BW_C':'9562','NGS_9773_BW_C': '9773',}, inplace=True)\n",
    "filtered_rows.index = ['1', '2', '3', '4', '5', '6', '7', '8', '8-2', '9', '10', '40', '35', '32']\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "0e29da3d-0e4a-419b-8b52-79a7c5e0f212",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>position</th>\n",
       "      <th>NGS_11298_BW_C</th>\n",
       "      <th>stdRange</th>\n",
       "      <th>NGS_3403_BW_C</th>\n",
       "      <th>stdRange_3403</th>\n",
       "      <th>NGS_5416_BW_C</th>\n",
       "      <th>stdRange_5416</th>\n",
       "      <th>NGS_8239_BW_C</th>\n",
       "      <th>stdRange_9412</th>\n",
       "      <th>NGS_9412_BW_C</th>\n",
       "      <th>stdRange_9562</th>\n",
       "      <th>NGS_9562_BW_C</th>\n",
       "      <th>stdRange_9773</th>\n",
       "      <th>NGS_9773_BW_C</th>\n",
       "      <th>avg</th>\n",
       "      <th>std</th>\n",
       "      <th>LT1stdNum</th>\n",
       "      <th>LT2stdNum</th>\n",
       "      <th>LT3stdNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038557</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.109266</td>\n",
       "      <td>191.0</td>\n",
       "      <td>1.620774</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.928758</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.364219</td>\n",
       "      <td>122.0</td>\n",
       "      <td>-0.855914</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-1.001602</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.912051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32038800</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.658015</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.486626</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.486626</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.073113</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-0.511789</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.560531</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>20.516254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039143</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.140251</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.436640</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-1.496028</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.264847</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-0.680013</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.250531</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>23.283876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039425</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.275396</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.331078</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-1.239280</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-1.055683</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-0.550791</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.009784</td>\n",
       "      <td>78.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>21.786846</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039555</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-1.454454</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.360618</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-0.835138</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.290891</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-1.398152</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.497329</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>17.761538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32039801</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-0.446895</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.861090</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.558681</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.141698</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.643092</td>\n",
       "      <td>39.0</td>\n",
       "      <td>-0.316096</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>15.290702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040110</td>\n",
       "      <td>53.0</td>\n",
       "      <td>-0.759058</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-0.184237</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-1.776048</td>\n",
       "      <td>102.0</td>\n",
       "      <td>-0.095803</td>\n",
       "      <td>68.0</td>\n",
       "      <td>-0.228454</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.876970</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.166667</td>\n",
       "      <td>22.615752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040421</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.707832</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.649195</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.034524</td>\n",
       "      <td>138.0</td>\n",
       "      <td>-0.356010</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-1.059654</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.004188</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.166667</td>\n",
       "      <td>39.792866</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32040533</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-0.491137</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.222831</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-1.242877</td>\n",
       "      <td>196.0</td>\n",
       "      <td>-0.040093</td>\n",
       "      <td>103.0</td>\n",
       "      <td>-1.017355</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.280650</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.666667</td>\n",
       "      <td>66.512321</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041111</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-0.576268</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.562039</td>\n",
       "      <td>48.0</td>\n",
       "      <td>-1.131193</td>\n",
       "      <td>263.0</td>\n",
       "      <td>-0.661641</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-0.405522</td>\n",
       "      <td>99.0</td>\n",
       "      <td>-0.291691</td>\n",
       "      <td>107.0</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>70.279798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32041480</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.090570</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-0.479851</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1.068759</td>\n",
       "      <td>76.0</td>\n",
       "      <td>-0.021811</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-1.003325</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.894268</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>15.282525</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32042500</td>\n",
       "      <td>326.0</td>\n",
       "      <td>0.626990</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.045877</td>\n",
       "      <td>103.0</td>\n",
       "      <td>-1.078117</td>\n",
       "      <td>518.0</td>\n",
       "      <td>-0.504651</td>\n",
       "      <td>178.0</td>\n",
       "      <td>-0.412896</td>\n",
       "      <td>190.0</td>\n",
       "      <td>-0.145278</td>\n",
       "      <td>225.0</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>130.783536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32043800</td>\n",
       "      <td>266.0</td>\n",
       "      <td>1.062362</td>\n",
       "      <td>137.0</td>\n",
       "      <td>-0.572366</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-1.548134</td>\n",
       "      <td>321.0</td>\n",
       "      <td>-0.179524</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.238662</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0.302023</td>\n",
       "      <td>206.0</td>\n",
       "      <td>182.166667</td>\n",
       "      <td>78.912223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>chr6</td>\n",
       "      <td>32045150</td>\n",
       "      <td>386.0</td>\n",
       "      <td>-0.493139</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.310888</td>\n",
       "      <td>306.0</td>\n",
       "      <td>-0.952764</td>\n",
       "      <td>722.0</td>\n",
       "      <td>-0.234600</td>\n",
       "      <td>431.0</td>\n",
       "      <td>-0.912547</td>\n",
       "      <td>313.0</td>\n",
       "      <td>-0.648263</td>\n",
       "      <td>359.0</td>\n",
       "      <td>471.833333</td>\n",
       "      <td>174.054988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr  position  NGS_11298_BW_C  stdRange  NGS_3403_BW_C  stdRange_3403  \\\n",
       "421   chr6  32038557           108.0  0.109266          191.0       1.620774   \n",
       "664   chr6  32038800            42.0 -0.658015           86.0       1.486626   \n",
       "1007  chr6  32039143             6.0 -2.140251           66.0       0.436640   \n",
       "1289  chr6  32039425            62.0  0.275396           85.0       1.331078   \n",
       "1419  chr6  32039555            20.0 -1.454454           70.0       1.360618   \n",
       "1665  chr6  32039801            42.0 -0.446895           62.0       0.861090   \n",
       "1974  chr6  32040110            53.0 -0.759058           66.0      -0.184237   \n",
       "2285  chr6  32040421            38.0 -0.707832           92.0       0.649195   \n",
       "2397  chr6  32040533            73.0 -0.491137          187.0       1.222831   \n",
       "2975  chr6  32041111            87.0 -0.576268          167.0       0.562039   \n",
       "3344  chr6  32041480            67.0  1.090570           43.0      -0.479851   \n",
       "4364  chr6  32042500           326.0  0.626990          250.0       0.045877   \n",
       "5664  chr6  32043800           266.0  1.062362          137.0      -0.572366   \n",
       "7014  chr6  32045150           386.0 -0.493139          700.0       1.310888   \n",
       "\n",
       "      NGS_5416_BW_C  stdRange_5416  NGS_8239_BW_C  stdRange_9412  \\\n",
       "421            51.0      -0.928758          146.0       0.364219   \n",
       "664            25.0      -1.486626           76.0       0.073113   \n",
       "1007           21.0      -1.496028           96.0       0.264847   \n",
       "1289           29.0      -1.239280           67.0      -1.055683   \n",
       "1419           31.0      -0.835138           65.0       0.290891   \n",
       "1665           25.0      -1.558681           72.0       0.141698   \n",
       "1974           30.0      -1.776048          102.0      -0.095803   \n",
       "2285           25.0      -1.034524          138.0      -0.356010   \n",
       "2397           23.0      -1.242877          196.0      -0.040093   \n",
       "2975           48.0      -1.131193          263.0      -0.661641   \n",
       "3344           34.0      -1.068759           76.0      -0.021811   \n",
       "4364          103.0      -1.078117          518.0      -0.504651   \n",
       "5664           60.0      -1.548134          321.0      -0.179524   \n",
       "7014          306.0      -0.952764          722.0      -0.234600   \n",
       "\n",
       "      NGS_9412_BW_C  stdRange_9562  NGS_9562_BW_C  stdRange_9773  \\\n",
       "421           122.0      -0.855914           55.0      -1.001602   \n",
       "664            57.0      -0.511789           45.0      -0.560531   \n",
       "1007           62.0      -0.680013           40.0      -0.250531   \n",
       "1289           33.0      -0.550791           44.0       1.009784   \n",
       "1419           51.0      -1.398152           21.0      -0.497329   \n",
       "1665           51.0      -0.643092           39.0      -0.316096   \n",
       "1974           68.0      -0.228454           65.0       0.876970   \n",
       "2285           52.0      -1.059654           24.0      -0.004188   \n",
       "2397          103.0      -1.017355           38.0      -0.280650   \n",
       "2975           81.0      -0.405522           99.0      -0.291691   \n",
       "3344           50.0      -1.003325           35.0       0.894268   \n",
       "4364          178.0      -0.412896          190.0      -0.145278   \n",
       "5664          168.0       0.238662          201.0       0.302023   \n",
       "7014          431.0      -0.912547          313.0      -0.648263   \n",
       "\n",
       "      NGS_9773_BW_C         avg         std  LT1stdNum  LT2stdNum  LT3stdNum  \n",
       "421            47.0  102.000000   54.912051        1.0        0.0        0.0  \n",
       "664            44.0   55.500000   20.516254        1.0        0.0        0.0  \n",
       "1007           50.0   55.833333   23.283876        1.0        0.0        0.0  \n",
       "1289           78.0   56.000000   21.786846        2.0        0.0        0.0  \n",
       "1419           37.0   45.833333   17.761538        1.0        0.0        0.0  \n",
       "1665           44.0   48.833333   15.290702        1.0        0.0        0.0  \n",
       "1974           90.0   70.166667   22.615752        1.0        0.0        0.0  \n",
       "2285           66.0   66.166667   39.792866        2.0        0.0        0.0  \n",
       "2397           87.0  105.666667   66.512321        2.0        0.0        0.0  \n",
       "2975          107.0  127.500000   70.279798        1.0        0.0        0.0  \n",
       "3344           64.0   50.333333   15.282525        2.0        0.0        0.0  \n",
       "4364          225.0  244.000000  130.783536        1.0        0.0        0.0  \n",
       "5664          206.0  182.166667   78.912223        1.0        0.0        0.0  \n",
       "7014          359.0  471.833333  174.054988        0.0        0.0        0.0  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_rows.insert(13, 'stdRange_9773', (filtered_rows['NGS_9773_BW_C'] - filtered_rows['avg']) / filtered_rows['std'])\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9abc455-f19f-4a85-8209-76d90e86aa4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5c39d8-4862-4130-b0dc-4864d534bbcc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "karl",
   "language": "python",
   "name": "karl"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
